855 citations · 3.9k across the 142 of their papers we have counts for
7 papers · 1 filter
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Erum Mushtaq, Chaoyang He, Jie Ding +1
Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulat…
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
Chaoyang He, Zhengyu Yang, Erum Mushtaq +3
Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data pr…
Secure Aggregation for Buffered Asynchronous Federated Learning
Jinhyun So, Ramy E. Ali, Başak Güler +1
Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure pr…
FairFed: Enabling Group Fairness in Federated Learning
Yahya H. Ezzeldin, Shen Yan, Chaoyang He +2
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as…
ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems
Mahdi Soleymani, Ramy E. Ali, Hessam Mahdavifar +1
Due to the surge of cloud-assisted AI services, the problem of designing resilient prediction serving systems that can effectively cope with stragglers/failures and minimize respon…
Basil: A Fast and Byzantine-Resilient Approach for Decentralized Training
Ahmed Roushdy Elkordy, Saurav Prakash, A. Salman Avestimehr
Detection and mitigation of Byzantine behaviors in a decentralized learning setting is a daunting task, especially when the data distribution at the users is heterogeneous. As our…